Agent Based Priority Heuristic for Job Scheduling on Computational Grids

Shah, Syed Nasir; Zakaria, Mohamed; Mahmood, Ahmad Kamil; Pal, Anindya Jyoti; Haron, Nazleeni Samiha

doi:10.1016/j.procs.2012.04.051

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…Several research works [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] have been reported on performance analysis and scheduling algorithms for cloud computing.…”

Section: Related Workmentioning

confidence: 99%

“…Several priority-based scheduling algorithms [15,16,[29][30][31][32][33][34] have been proposed for grid and cloud computing. In [29], Bansal et al used priority in a static round robin scheduling algorithm to improve the performance.…”

Section: Related Workmentioning

confidence: 99%

“…In [29], Bansal et al used priority in a static round robin scheduling algorithm to improve the performance. Authors in [32,33] dynamically scheduled tasks by considering QoS and the performance metrics such as delay, deadline, and execution times. In [34], Chtourou et al considered resource constrains and apply two-phase (priority and threshold) to optimize the performance.…”

Section: Related Workmentioning

confidence: 99%

“…In [34], Chtourou et al considered resource constrains and apply two-phase (priority and threshold) to optimize the performance. All of the works in [29,[32][33][34] are priority-based scheduling in grid computing. However, adapting grid in cloud creates some efficiency problems, such as finding a server in grid to fully satisfy resource requirements for a type application while finding several servers in cloud to do so.…”

Section: Related Workmentioning

confidence: 99%

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Scheduling internet of things applications in cloud computing

et al. 2016

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Internet of Things (IoT) is one of the greatest technology revolutions in the history. Due to IoT potential, daily objects will be consciously worked in harmony with optimized performances. However, today, technology is not ready to fully bring its power to our daily life because of huge data analysis requirements in instant time. On the other hand, the powerful data management of cloud computing gives IoT an opportunity to make the revolution in our life. However, the traditional cloud computing server schedulers are not ready to provide services to IoT because IoT consists of a number of heterogeneous devices and applications which are far away from standardization. Therefore, to meet the expectations of users, the traditional cloud computing server schedulers should be improved to efficiently schedule and allocate IoT requests. There are several proposed scheduling algorithms for cloud computing in the literature. However, these scheduling algorithms are limited because of considering neither heterogeneous servers nor dynamic scheduling approach for different priority requests. Our objective is to propose Husnu S. Narman dynamic dedicated server scheduling for heterogeneous and homogeneous systems to efficiently provide desired services by considering priorities of requests. Results show that the proposed scheduling algorithm improves throughput up to 40 % in heterogeneous and homogeneous cloud computing systems for IoT requests. Our proposed scheduling algorithm and related analysis will help cloud service providers build efficient server schedulers which are adaptable to homogeneous and heterogeneous environments by considering system performance metrics, such as drop rate, throughput, and utilization in IoT.

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“…Several research works [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] have been reported on performance analysis and scheduling algorithms for cloud computing.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Scheduling internet of things applications in cloud computing

et al. 2016

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“…Shah et. al [17] proposed job scheduling technique based on agent used for effective and efficient execution of user jobs. This algorithm mainly account on Quality of Service (QoS) parameters like waiting time, turnaround time, response time, total completion time, etc.…”

Section: G) Agent Based Priority Heuristic For Job Scheduling On Compmentioning

confidence: 99%

Job Scheduling Algorithms in Cloud Computing: A Survey

Goel¹,

Chamoli²

2014

IJCA

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Cloud Computing is the new IT Paradigm that makes the delivery of computing resources (Hardware and Software), applications and data as a service over the internet to its users. Cloud computing mainly intended to provide reliable, dynamic and virtualized services in terms of resources for doing computation, storage and knowledge sharing. An essential requirement in cloud computing is scheduling of current jobs to be executed within some given metrics or constraints. In Cloud computing, execution of jobs requires various resources which are available to them by fulfilling certain constraints like best performance, minimum execution time, shortest response time, fault-tolerance and quality of expected services. The scheduler should order the jobs in a way where the balance between improving the quality of services and at the same time maintaining the efficiency and fairness among the jobs.

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